MATH-1335 Use new RNG API.
Project: http://git-wip-us.apache.org/repos/asf/commons-math/repo Commit: http://git-wip-us.apache.org/repos/asf/commons-math/commit/f9f632e7 Tree: http://git-wip-us.apache.org/repos/asf/commons-math/tree/f9f632e7 Diff: http://git-wip-us.apache.org/repos/asf/commons-math/diff/f9f632e7 Branch: refs/heads/develop Commit: f9f632e7056cd9d2b3c88bad50fe0597610e2eca Parents: 0484bdb Author: Gilles <er...@apache.org> Authored: Thu May 12 12:43:04 2016 +0200 Committer: Gilles <er...@apache.org> Committed: Tue May 17 15:30:23 2016 +0200 ---------------------------------------------------------------------- .../commons/math4/filter/KalmanFilterTest.java | 19 ++++++++----------- 1 file changed, 8 insertions(+), 11 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/commons-math/blob/f9f632e7/src/test/java/org/apache/commons/math4/filter/KalmanFilterTest.java ---------------------------------------------------------------------- diff --git a/src/test/java/org/apache/commons/math4/filter/KalmanFilterTest.java b/src/test/java/org/apache/commons/math4/filter/KalmanFilterTest.java index 2db564d..131da51 100644 --- a/src/test/java/org/apache/commons/math4/filter/KalmanFilterTest.java +++ b/src/test/java/org/apache/commons/math4/filter/KalmanFilterTest.java @@ -27,9 +27,6 @@ import org.apache.commons.math4.linear.MatrixDimensionMismatchException; import org.apache.commons.math4.linear.MatrixUtils; import org.apache.commons.math4.linear.RealMatrix; import org.apache.commons.math4.linear.RealVector; -import org.apache.commons.math4.random.RandomGenerator; -import org.apache.commons.math4.random.JDKRandomGenerator; -import org.apache.commons.math4.rng.UniformRandomProvider; import org.apache.commons.math4.rng.RandomSource; import org.apache.commons.math4.util.FastMath; import org.apache.commons.math4.util.Precision; @@ -129,19 +126,20 @@ public class KalmanFilterTest { RealVector pNoise = new ArrayRealVector(1); RealVector mNoise = new ArrayRealVector(1); - RandomGenerator rand = new JDKRandomGenerator(); + final RealDistribution.Sampler rand = new NormalDistribution().createSampler(RandomSource.create(RandomSource.WELL_19937_C)); + // iterate 60 steps for (int i = 0; i < 60; i++) { filter.predict(); // Simulate the process - pNoise.setEntry(0, processNoise * rand.nextGaussian()); + pNoise.setEntry(0, processNoise * rand.sample()); // x = A * x + p_noise x = A.operate(x).add(pNoise); // Simulate the measurement - mNoise.setEntry(0, measurementNoise * rand.nextGaussian()); + mNoise.setEntry(0, measurementNoise * rand.sample()); // z = H * x + m_noise RealVector z = H.operate(x).add(mNoise); @@ -217,7 +215,7 @@ public class KalmanFilterTest { double[] expectedInitialState = new double[] { 0.0, 0.0 }; assertVectorEquals(expectedInitialState, filter.getStateEstimation()); - RandomGenerator rand = new JDKRandomGenerator(); + final RealDistribution.Sampler rand = new NormalDistribution().createSampler(RandomSource.create(RandomSource.WELL_19937_C)); RealVector tmpPNoise = new ArrayRealVector( new double[] { FastMath.pow(dt, 2d) / 2d, dt }); @@ -227,13 +225,13 @@ public class KalmanFilterTest { filter.predict(u); // Simulate the process - RealVector pNoise = tmpPNoise.mapMultiply(accelNoise * rand.nextGaussian()); + RealVector pNoise = tmpPNoise.mapMultiply(accelNoise * rand.sample()); // x = A * x + B * u + pNoise x = A.operate(x).add(B.operate(u)).add(pNoise); // Simulate the measurement - double mNoise = measurementNoise * rand.nextGaussian(); + double mNoise = measurementNoise * rand.sample(); // z = H * x + m_noise RealVector z = H.operate(x).mapAdd(mNoise); @@ -394,8 +392,7 @@ public class KalmanFilterTest { final MeasurementModel mm = new DefaultMeasurementModel(H, R); final KalmanFilter filter = new KalmanFilter(pm, mm); - final UniformRandomProvider rng = RandomSource.create(RandomSource.WELL_19937_C, 1000); - final RealDistribution.Sampler dist = new NormalDistribution(0, measurementNoise).createSampler(rng); + final RealDistribution.Sampler dist = new NormalDistribution(0, measurementNoise).createSampler(RandomSource.create(RandomSource.WELL_19937_C, 1000)); for (int i = 0; i < iterations; i++) { // get the "real" cannonball position
